Summary of Discovering Invariant Neighborhood Patterns For Heterophilic Graphs, by Ruihao Zhang et al.
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs
by Ruihao Zhang, Zhengyu Chen, Teng Xiao, Yueyang Wang, Kun Kuang
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the issue of distribution shifts on non-homophilous graphs, a problem that existing graph neural network methods fail to account for. Most current methods rely on the homophilous assumption, where nodes from the same class are more likely to be linked. However, this assumption doesn’t always hold in real-world graphs, resulting in complex distribution shifts. The authors propose Invariant Neighborhood Pattern Learning (INPL) to alleviate this problem, comprising an Adaptive Neighborhood Propagation (ANP) module and an Invariant Non-Homophilous Graph Learning (INHGL) module. The ANP module captures adaptive neighborhood information, while the INHGL module constrains the ANP to learn invariant graph representations on non-homophilous graphs. Experimental results on real-world graphs demonstrate that INPL achieves state-of-the-art performance for learning on large non-homophilous graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in computer science called “distribution shifts” on special kinds of networks called graphs. Graphs are like maps, but instead of roads and cities, they have nodes and links between them. Most current methods for working with these networks assume that the nodes are connected in a certain way, which isn’t always true in real-life situations. The authors propose a new method to make their approach more flexible and accurate, which performs better than existing methods on large, complex networks. |
Keywords
* Artificial intelligence * Graph neural network
